The present invention relates to dissemination of education material in general, and to methods, systems, and media of developing such educational materials through an online collaboration environment.
With developments in the education industry, students seek access to course-related information and their own course work, anywhere, anytime. Student want current, relevant, interesting and engaging course materials and assignments taught by teachers, instructors, counselors and advisors who are aware of student's educational and professional path and goals based on a clear map of course progress and degree program. Enabling and facilitating students' online activities around their campus is a major consideration in providing the desired student experience.
Online education now demands providing educational services to a diverse global audience from different cultural backgrounds. Education providers face the challenge of providing high quality education across a diverse student population. Educational programs must provide skills that students can apply in their lives and professions to make a real difference in the real world. Educators must strive to create a community of learners connected to one another.
A learning management system (LMS), as referred to in the art, is software for delivering, tracking and managing training of students. LMSs range from systems for managing student training records to software for distributing courses over the Internet and offering features for online collaboration. In many instances, LMSs are used to automate record-keeping as well as to register students for classroom and online courses. Self-registration, faculty-led learning, learning workflow, the provision of on-line learning (e.g., read and understand), on-line assessment, management of continuous professional education (CPE), collaborative learning (e.g., application sharing, discussion threads), and learning resource management (e.g., instructors, facilities, equipment), are various aspects of LMSs.
FIG. 1 is a diagram depicting a known LMS 10, including one offered by Blackboard, WebCT, Moodle, eCollege and others, which allows a faculty member to place his or her courses, in whole or in part, online. As depicted, the faculty 12 plays a central role for mediating between a student 13, presenting course content 15 and assessing a student 14. LMSs 10 usually provide all-inclusive learning environments for faculty and students, with the faculty 12 disseminating instructional material specific to a course of study amongst students. As such, the faculty member serves as the facilitator, assessor and content developer.
Conceptually, there is no difference between the role of a teacher in conventional LMSs 10 and the role of a teacher in a bricks and mortar classroom. In both cases, the students are grouped and assigned a specific teacher. The teacher introduces all course content and materials into the classroom and mediates and assesses the learning process of the student. Thus, under LMS 10, the web is a tool to replicate, as closely as possible, the traditional classroom environment and the LMS 10 is limited by its system boundaries, just as the physical classroom is limited by four walls and doors.
With advances in content and media delivery technologies, the LMS model has not fully taken advantage of the available features for educating students. For example, such advances allow students to access educational content not only via laptops and desktops, but also smart phones, PDA's, iPods, Netbooks and eBooks. It is, for example, estimated that the majority of prospective student market has a smart phone or PDA, with advances content delivery capabilities via downloadable applications or by content streaming. These new devices have enabled users access to podcasting, wikis, blogs, web cams, eBook readers, MP3 players, social networks and virtual learning environments.
Conventional LMS developers' attempt in incorporating new features into their existing systems in some cases can result in significant developments cost in redesigning their content to incorporate the functionality of these new technologies. In other cases, the developers may have to open up their system platform through application programming interfaces (API's) to “bolt on” new technological capabilities. LMS redesign investment may be expensive, especially when new development work may not be able to keep up with the proliferation of ever advancing technologies and features. Opening up platforms through APIs may present a significant competitive disadvantage to LMS vendors and service providers who have invested heavily in their proprietary instructional material delivery systems.
Additionally, educational services are increasingly offered over global networks of institutions and universities. For example, Laureate Education Inc., the assignee of the present application, currently offers accredited campus-based and online courses in a wide variety of programs, including undergraduate and graduate degree programs and specializations, to nearly a wide range of students in numerous countries. Such a global educational network requires supporting learning environments that are tailored to bring to students a global perspective blended with a local point of view, creating a truly multicultural, career-oriented educational experience for students. For example, the educational experience may be a career-focused or licensing program, a multi-year undergraduate degree program, or master's and/or doctorate degree program in any one of a number of fields including engineering, education, business, health care, hospitality, architecture, and information technology, etc.
Laureate Education Inc.'s U.S. Patent Publication No. US 2009-0291426 A1, the entire contents of which are hereby incorporated by reference, discloses an “Educational System For Presenting One Or More Learning Units To Students In Different Learning Environments”, where each unit is associated with an assessment information relating to students. A digital rights and asset management application controls access to the content associated with each one of said one or more units according to corresponding unit identifiers. An assessment application, e.g., a grade book application, stores assessment information derived from presenting the content to said one or more users in the first and second interactive environments, with the unit identifier correlating the assessment information with the units.
Laureate Education Inc.'s U.S. Patent Publication No. 2009-0311658 A1, the entire contents of which are hereby incorporated by reference, discloses “System And Method For Collaborative Development Of Online Courses And Programs Of Study” over a social network. A database stores an initial framework that defines a sequence of learning units for creating a desired learning environment for students. The learning units are identified by corresponding learning unit identifiers. A plurality of workstations coupled to the network are used for entry of reviewer information by the participants using the learning environment created for the students. The reviewer information comprise one or more comments entered by one participant about a learning unit and a rank entered by anther participant about the comment, with the rank being correlated with a defined ranking standard. A processor processes the rank according to a predefined criteria to produce a ranking result that is associated with a learning unit identifier. The ranking result is used for associating learning content to the learning unit identified by the learning unit identifier.
The conventional learning process also involves receiving and responding to facial expressions from students. A facial expression is a visible manifestation of the affective state, cognitive activity, intention, personality, and psychopathology of a person. Facial expressions convey non-verbal cues and play an important role in the instructional setting. These cues may indicate that the student is perplexed, bored, excited, happy, thoughtful, frustrated, or a wide range of other emotions. Instructors in the conventional learning process use these cues as feedback and adapt lessons accordingly to meet their students' needs.
Also known are facial recognition software systems (FRSS). The paper “Facial Expression Recognition: A Brief Tutorial Overview” by Chibelushi and Bourel, the entire contents of which are hereby incorporated by reference, provides an overview of FRSS. Although humans recognize facial expressions virtually without any effort or dealy, reliable expression recognition by machines is still a challenge. Several different approaches are known to overcome these challenges. These approaches include those described by U.S. Pat. No. 6,690,814 B1 to Yuasa et al., the entire contents of which are hereby incorporated by reference, and the article “Spontaneous Emotional Facial Expression Detection” by Zeng et al., Journal of Multimedia, Vol. 1, No. 5, August 2006, the entire contents of which are hereby incorporated by reference.
FIG. 2 illustrates a logic flow diagram depicting a known process for classifying facial expressions. The process in the FRSS 200 begins by acquiring an image 200. The image may be acquired from an input device, and the input device may be a camera, a video recorder, an integrated camera, a file, a streaming video source, a computer, a portable computer, a mobile device, a phone, or any other source capable of supplying an image. The image may be in the form of raw data 220. The image may be user image data 215. Pre-processing 230 may be performed on the raw data 220 to perform face segmentation. An example of face segmentation is shown in element 235. After pre-processing 235, feature extraction 240 may be performed, which converts pixel data in a higher-level representation, for example, shape, motion, color, texture, or spatial configuration of the face or its components. The extracted features are represented by feature vector 242, which includes basis vectors 244 and weights 246. Feature data may include feature vector 242, basis vectors 244, or weights 246. Classification 250 may be performed on the feature vector 242. Classification 250 uses a model 255 to determine which emotion 257 is present in the image. Post-processing 260 may be performed on the output from the classifications 250. Post-processing uses techniques to improve recognition and includes techniques of exploiting domain knowledge to correct classification errors or coupling together several levels of classification hierarchy. The process in FRSS 200 produces an emotion value 270.
Also known are avatars. As used herein, an avatar is the graphical representation of the user or the user's alter ego or character. It may take either a three-dimensional form, as in games or virtual worlds, or a two-dimensional form as an icon in Internet forums and other online communities. It can also refer to a text construct. An avatar is an object representing the user. An avatar may be as simple as a smiley face or as complex as a virtual face. U.S. Pat. No. 7,751,599 B2 to Chen et al., the contents of which are hereby incorporated by reference, describes performing facial recognition to and converting the facial recognition into avatars.
With advances in information technologies, there exists a need for an educational system that can easily implement advances in learning technology for responding to users' facial expressions during the presentation of course content.